





import operator



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase = False	,_lowerCAmelCase = None  ):
   '''simple docstring'''


   A_  :					Tuple         =						operator.lt if reverse else operator.gt
   A_  :					int         =						solution or []

   if not arr:
      return solution

   A_  :					Any         =						[arr.pop(0  )]
   for i, item in enumerate(a__  ):
      if _operator(a__	,sublist[-1]  ):
         sublist.append(a__  )
         arr.pop(a__  )

    #  merging sublist into solution list
   if not solution:
      solution.extend(a__  )
   else:
      while sublist:
         A_  :					int         =						sublist.pop(0  )
         for i, xx in enumerate(a__  ):
            if not _operator(a__	,a__  ):
               solution.insert(a__	,a__  )
               break
         else:
            solution.append(a__  )

   strand_sort(a__	,a__	,a__  )
   return solution


if __name__ == "__main__":
      assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
      assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]














"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)